Machine learning post-hoc interpretability: a systematic mapping study (2022)
Source: SBSI: XVIII Brazilian Symposium on Information Systems. Conference titles: Brazilian Symposium on Information Systems. Unidade: EACH
Subjects: APRENDIZADO COMPUTACIONAL, INTELIGÊNCIA ARTIFICIAL, TOMADA DE DECISÃO
ABNT
VIEIRA, Carla Piazzon Ramos e DIGIAMPIETRI, Luciano Antonio. Machine learning post-hoc interpretability: a systematic mapping study. 2022, Anais.. New York, NY: ACM, 2022. p. art. 1 ( 1-8). Disponível em: https://doi.org/10.1145/3535511.3535512. Acesso em: 31 ago. 2024.APA
Vieira, C. P. R., & Digiampietri, L. A. (2022). Machine learning post-hoc interpretability: a systematic mapping study. In SBSI: XVIII Brazilian Symposium on Information Systems (p. art. 1 ( 1-8). New York, NY: ACM. doi:10.1145/3535511.3535512NLM
Vieira CPR, Digiampietri LA. Machine learning post-hoc interpretability: a systematic mapping study [Internet]. SBSI: XVIII Brazilian Symposium on Information Systems. 2022 ;art. 1 ( 1-8).[citado 2024 ago. 31 ] Available from: https://doi.org/10.1145/3535511.3535512Vancouver
Vieira CPR, Digiampietri LA. Machine learning post-hoc interpretability: a systematic mapping study [Internet]. SBSI: XVIII Brazilian Symposium on Information Systems. 2022 ;art. 1 ( 1-8).[citado 2024 ago. 31 ] Available from: https://doi.org/10.1145/3535511.3535512